Image fusion for spatial enhancement of hyperspectral image via pixel group based non-local sparse representation

Jing Yang, Ying Li, Jonathan Cheung-Wai Chan, Qiang Shen

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19 Citations (SciVal)
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Abstract

Restricted by technical and budget constraints, hyperspectral images (HSIs) are usually obtained with low spatial resolution. In order to improve the spatial resolution of a given hyperspectral image, a new spatial and spectral image fusion approach via pixel group based non-local sparse representation is proposed, which exploits the spectral sparsity and spectral non-local
self-similarity of the hyperspectral image. The proposed approach fuses the hyperspectral image with a high-spatial-resolution multispectral image of the same scene to obtain a hyperspectral image with high spatial and spectral resolutions. The input hyperspectral image is used to train the spectral dictionary, while the sparse codes of the desired HSI are estimated by jointly encoding the similar pixels in each pixel group extracted from the high-spatial-resolution multispectral image. To improve the accuracy of the pixel group based non-local sparse representation, the similar pixels in a pixel group are selected by utilizing both the spectral and spatial information. The performance
of the proposed approach is tested on two remote sensing image datasets. Experimental results suggest that the proposed method outperforms a number of sparse representation based fusion techniques, and can preserve the spectral information while recovering the spatial details under large magnification factors.
Original languageEnglish
JournalRemote Sensing
Volume9
Issue number1
DOIs
Publication statusPublished - 09 Jan 2017

Keywords

  • spatial and spectral image fusion
  • spectral dictionary learning
  • spectral non-local self-similarity
  • pixel based non-local sparse representation

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